/Paper-with-Code-of-Wireless-communication-Based-on-DL

无线与深度学习结合的论文代码整理/Paper-with-Code-of-Wireless-communication-Based-on-DL

For English reader,please refer to English Version.

随着深度学习的发展，使用深度学习解决相关通信领域问题的研究也越来越多。作为一名通信专业的研究生，如果实验室没有相关方向的代码积累，入门并深入一个新的方向会十分艰难。同时，大部分通信领域的论文不会提供开源代码，reproducible research比较困难。
基于深度学习的通信论文这几年飞速增加，明显能感觉这些论文的作者更具开源精神。本项目专注于整理在通信中应用深度学习，并公开了相关源代码的论文。
个人关注的领域和精力有限，这个列表不会那么完整。如果你知道一些相关的开源论文，但不在此列表中，非常欢迎添加，为community贡献一份力量。欢迎交流^_^
温馨提示:watch相较于star更容易得到更新通知 。
TODO

  • 按不同小方向分类
  • 论文添加下载链接
  • 增加更多相关论文代码
  • 传统通信论文代码列表
  • “通信+DL”论文列表（引用较高，可以没有代码）

论文/Paper

Paper Code
Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding RLdecoding
Adaptive Neural Signal Detection for Massive MIMO mehrdadkhani/MMNet
Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks DynamicMultiChannelRL
Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells Q-Learning-Power-Control
Spectrum sharing in vehicular networks based on multi-agent reinforcement learning MARLspectrumSharingV2X
Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors modulation_classif
Learning Physical-Layer Communication with Quantized Feedback quantizedfeedback
Decentralized Scheduling for Cooperative Localization with Deep Reinforcement Learning DeepRLVehicularLocalization
Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks DynamicMultiChannelRL
MIST: A Novel Training Strategy for Low-latencyScalable Neural Net Decoders MIST_CNN_Decoder
Deep UL2DL: Channel Knowledge Transfer from Uplink to Downlink UL2DL
Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency DL-Massive-MIMO
Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks DPPL
Learning Based Power Control for mmWave Massive MIMO against Jamming Learning-Based-Power-Control-for-mmWave-Massive-MIMO-against-Jamming
Sparsely Connected Neural Network for Massive MIMO Detection MIMO_Detection
Power Allocation in Multi-Cell Networks Using Deep Reinforcement Learningg qfnet
Deep Learning in Downlink Coordinated Multipoint in New Radio Heterogeneous Networks DL-CoMP-Machine-Learning
Deep Reinforcement Learning for Resource Allocation in V2V Communications https://github.com/haoyye/ResourceAllocationReinforcementLearning
RF-based Direction Finding of UAVs Using DNN https://github.com/LahiruJayasinghe/DeepDOA
Deepcode: Feedback Codes via Deep Learning https://github.com/hyejikim1/Deepcode
https://github.com/yihanjiang/feedback_code
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems https://github.com/meysamsadeghi/Security-and-Robustness-of-Deep-Learning-in-Wireless-Communication-Systems
AIF: An Artificial Intelligence Framework for Smart Wireless Network Management caogang/WlanDqn
Deep-Learning-Power-Allocation-in-Massive-MIMO lucasanguinetti / Deep-Learning-Power-Allocation-in-Massive-MIMO
DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications The DeepMIMO Dataset
Fast Deep Learning for Automatic Modulation Classification dl4amc/source
Deep Learning-Based Channel Estimation Mehran-Soltani/ChannelNet
Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication seotaijiya/TPC_D2D
Deep learning-based channel estimation for beamspace mmWave massive MIMO systems hehengtao/LDAMP_based-Channel-estimation
Spatial deep learning for wireless scheduling willtop/Spatial_DeepLearning_Wireless_Scheduling
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach swordest/mec_drl
A deep-reinforcement learning approach for software-defined networking routing optimization knowledgedefinednetworking / a-deep-rl-approach-for-sdn-routing-optimization
Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells farismismar / Q-Learning-Power-Control
Deep Learning for Optimal Energy-Efficient Power Control in Wireless Interference Networks bmatthiesen / deep-EE-opt
Actor-Critic-Based Resource Allocation for Multi-modal Optical Networks BoyuanYan / Actor-Critic-Based-Resource-Allocation-for-Multimodal-Optical-Networks
Deep MIMO Detection neevsamuel/DeepMIMODetection
Learning to Detect neevsamuel/LearningToDetect
An iterative BP-CNN architecture for channel decoding liangfei-info/Iterative-BP-CNN
On Deep Learning-Based Channel Decoding gruberto/DL-ChannelDecoding
Decoder-using-deep-learning
DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls ruihuili / DELMU
Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement farismismar / Deep-Q-Learning-SON-Perf-Improvement
An Introduction to Deep Learning for the Physical Layer yashcao / RTN-DL-for-physical-layer
musicbeer / Deep-Learning-for-the-Physical-Layer
helloMRDJ / autoencoder-for-the-Physical-Layer
Convolutional Radio Modulation Recognition Networks chrisruk/cnn
qieaaa / Singal-CNN
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex Convolutional Networks zhongyuanzhao / dl_ofdm
Joint Transceiver Optimization for WirelessCommunication PHY with Convolutional NeuralNetwork hlz1992/RadioCNN
Deep Learning for Massive MIMO CSI Feedback sydney222 / Python_CsiNet
Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning TianLin0509/BF-design-with-DL
5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning lasseufpa/5gm-data
Deep multi-user reinforcement learning for dynamic spectrum access in multichannel wireless networks shkrwnd/Deep-Reinforcement-Learning-for-Dynamic-Spectrum-Access
DeepNap: Data-Driven Base Station Sleeping Operations through Deep Reinforcement Learning zaxliu/deepnap
Automatic Modulation Classification: A Deep Learning Enabled Approach mengxiaomao/CNN_AMC
Deep Architectures for Modulation Recognition qieaaa / Deep-Architectures-for-Modulation-Recognition
Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks mkoz71 / Energy-Efficiency-in-Reinforcement-Learning
Learning to optimize: Training deep neural networks for wireless resource management Haoran-S / DNN_WMMSE
Implications of Decentralized Q-learning Resource Allocation in Wireless Networks wn-upf / decentralized_qlearning_resource_allocation_in_wns
Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems haoyye/OFDM_DNN

"通信+DL"论文（无代码)/Paper List Without Code

说明：论文主要来源于arxiv中Signal Processing和Information Theory

数据集/Database

To the best of our knowledge,this is the first open dataset of real modulated signals for wireless communication systems.

The dataset is collected in real physical environment, and the channel suffers from many factors such as limited LED bandwidth, multi-reflection,spurious or continuous jamming, etc.

学者个人主页/Researcher Homepage

  • Ahmed Alkhateeb:Research Interests
    • Millimeter Wave and Massive MIMO Communication
    • Vehicular and Drone Communication Systems
    • Applications of Machine Learning in Wireless Communication
    • Building Mobile Communication Systems that Work in Reality!
  • Emil Björnson: He performs research on multi-antenna communications, Massive MIMO, radio resource allocation, energy-efficient communications, and network design.

有用的网页和材料/Useful Websites and Materials


贡献者/Contributors：


版本更新/Version Update：

  1. 第一版完成/First Version：2019-02-21